Spark Drivers & Connectors
for Data Integration

The Spark driver acts like a bridge that facilitates communication between various applications and Spark, allowing the application to read data as if it were a relational database. The Spark driver abstracts the complexities of Spark APIs, authentication methods, and data types, making it simple for any application to connect to Spark data in real-time via standard SQL queries.

Working with a Spark Driver is different than connecting with Spark through other means. Spark API integrations require technical experience from a software developer or IT resources. Additionally, due to the constant evolution of APIs and services, once you build your integration you have to constantly maintain Spark integration code moving forward.

By comparison, our Spark Drivers offer codeless access to live Spark data for both technical and non-technical users alike. Any user can install our drivers and begin working with live Spark data from any client application. Because our drivers conform to standard data interfaces like ODBC, JDBC, ADO.NET etc. they offer a consistent, maintenance-free interface to Spark data. We manage all of the complexities of Spark integration within each driver and deploy updated drivers as systems evolve so your applications continue to run seamlessly.

If you need truly zero-maintenance integration, check out connectivity to Spark via CData Connect Cloud. With Connect Cloud you can configure all of your data connectivity in one place and connect to Spark from any of the available Cloud Drivers and Client Applications. Connectivity to Spark is managed in the cloud, and you never have to worry about installing new drivers when Spark is updated.

Many organizations draw attention to their library of connectors. After all, data connectivity is a core capability needed for applications to maximize their business value. However, it is essential to understand exactly what you are getting when evaluating connectivity. Some vendors are happy to offer connectors that implement basic proof-of-concept level connectivity. These connectors may highlight the possibilities of working with Spark, but often only provide a fraction of capability. Finding real value from these connectors usually requires additional IT or development resources.

Unlike these POC-quality connectors, every CData Spark driver offers full-featured Spark data connectivity. The CData Spark drivers support extensive Spark integration, providing access to all of the Spark data and meta-data needed by enterprise integration or analytics projects. Each driver contains a powerful embedded SQL engine that offers applications easy and high-performance access to all Spark data. In addition, our drivers offer robust authentication and security capabilities, allowing users to connect securely across a wide range of enterprise configurations. Compare drivers and connectors to read more about some of the benefits of CData's driver connectivity.

With our drivers and connectors, every data source is essentially SQL-based. The CData Spark driver contains a full SQL-92 compliant engine that translates standard SQL queries into Spark API calls dynamically. Queries are parsed and optimized for each data source, pushing down as much of the request to Spark as possible. Any logic that can not be pushed to Spark is handled transparently client-side by the driver/connector engine. Ultimately, this means that Spark looks and acts exactly like a database to any client application or tool. Users can integrate live Spark connectivity with ANY software solution that can talk to a standard database.

Using the CData Spark drivers and connectors, Spark can be easily integrated with almost any application. Any software or technology that can integrate with a database or connect with standards-based drivers like ODBC, JDBC, ADO.NET, etc., can use our drivers for live Spark data connectivity. Explore some of the more popular Spark data integrations online.

Additionally, since Spark supported by CData Connect Cloud, we enable all kinds of new Spark cloud integrations.

Spark Analytics and Spark Cloud BI integration is universally supported for BI and data science. In addition, CData provides native client connectors for popular analytics applications like Power BI, Tableau, and Excel that simplify Spark data integration. Additionally, native Python connectors are widely available for data science and data engineering projects that integrate seamlessly with popular tools like Pandas, SQLAlchemy, Dash, and Petl.

Spark data integration is typically enabled with CData Sync, a robust any-to-any data pipeline solution that is easy to set up, runs everywhere, and offers comprehensive enterprise-class features for data engineering. CData Sync makes it easy to replicate Spark data any database or data warehouse, and maintain parity between systems with automated incremental Spark replication. In addition, our Spark drivers and connectors can be easily embedded into a wide range of data integration tools to augment existing solutions.

Absolutely. The best way to integrate Spark with Excel is by using the CData Connect Cloud Excel Add-In. The Spark Excel Add-In provides easy Spark integration directly from Microsoft Excel Desktop, Mac, or Web (Excel 365). Simply configure your connection to Spark from the easy-to-use cloud interface, and access Spark just like you would another native Excel data source.